Anthony Mark Phillips

Bio: Anthony Mark Phillips is an academic researcher from Ford Motor Company. The author has contributed to research in topics: Electric vehicle & Powertrain. The author has an hindex of 21, co-authored 51 publications receiving 3809 citations.

Predictive energy management of a power-split hybrid electric vehicle

Abstract: In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.

MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle

Abstract: A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by utilizing a planetary gear set to split and combine the power produced by electric machines and a combustion engine. Because of the different modes of operation, devising a near optimal energy management strategy is quite challenging and essential for these vehicles. To improve the fuel economy of a power-split HEV, we first formulate the energy management problem as a nonlinear and constrained optimal control problem. Then two different cost functions are defined and model predictive control (MPC) strategies are utilized to obtain the power split between the combustion engine and electrical machines and the system operating points at each sample time. Simulation results on a closed-loop high-fidelity model of a power-split HEV over multiple standard drive cycles and with different controllers are presented. The results of a nonlinear MPC strategy show a noticeable improvement in fuel economy with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and the other proposed methodology by the authors based on a linear time-varying MPC.

Intelligent Hybrid Vehicle Power Control—Part I: Machine Learning of Optimal Vehicle Power

Abstract: In this series of two papers, we present our research on intelligent energy management for hybrid electric vehicles (HEVs). These two papers cover the modeling of power flow in HEVs, the mathematical background of optimization in energy management in HEVs, a machine learning framework that combines dynamic programming (DP) with machine learning to learn about roadway-type- and traffic-congestion-level-specific energy optimization, machine learning algorithms, and real-time quasi-optimal control of energy flow in an HEV. This first paper presents our research on machine learning for optimal energy management in HEVs. We will present a machine learning framework ML_EMO_HEV developed for the optimization of energy management in an HEV, machine learning algorithms for predicting driving environments, and the generation of an optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratory's Powertrain Systems Analysis Toolkit (PSAT). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.

Intelligent Hybrid Vehicle Power Control—Part II: Online Intelligent Energy Management

Abstract: This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). In the first paper, we presented the machine-learning framework ML_EMO_HEV, which was developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine-learning algorithms for predicting driving environments and generating the optimal power split of the HEV system for a given driving environment. In this paper, we present the following three online intelligent energy controllers: 1) IEC_HEV_SISE; 2) IEC_HEV_MISE ; and 3) IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine-learning framework ML_EMO_HEV to generate the best combination of engine power and battery power in real time such that the total fuel consumption over the whole driving cycle is minimized while still meeting the driver's demand and the system constraints, including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape hybrid vehicle model for online performance evaluation. Based on their performances on ten test drive cycles provided by the Powertrain Systems Analysis Toolkit library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy control. The best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm with multiple initial SOC points and single ending point, can provide fuel savings ranging from 5% to 19%. Together, these two papers cover the innovative technologies for modeling power flow, mathematical background of optimization in energy management, and machine-learning algorithms for generating intelligent energy controllers for quasioptimal energy flow in a power-split HEV.

Anomaly detection: A survey

Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends

Abstract: As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

Abstract: This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.